Description Usage Arguments Details Value Author(s) See Also Examples

Winsorizing a vector means that a predefined quantum of the smallest and/or the largest values are replaced by less extreme values. Thereby the substitute values are the most extreme retained values.

1 2 |

`x` |
a numeric vector to be winsorized. |

`minval` |
the low border, all values being lower than this will be replaced by this value. The default is set to the 5%-quantile of x. |

`maxval` |
the high border, all values being larger than this will be replaced by this value. The default is set to the 95%-quantile of x. |

`probs` |
numeric vector of probabilities with values in [0,1] as used in |

`na.rm` |
should NAs be omitted to calculate the quantiles? |

`type` |
an integer between 1 and 9 selecting one of the nine quantile algorithms detailed in |

The winsorized vector is obtained by

*wins(x) = -c if x < -c,
c if x > c, x otherwise*

Consider standardizing (possibly robust) the data before winsorizing.

A vector of the same length as the original data
`x`

containing the winsorized data.

Andri Signorell <[email protected]>

`Winsorize`

from the package `robustHD`

contains an option to winsorize multivariate data

1 2 3 4 5 6 7 8 9 10 11 | ```
## generate data
set.seed(1234) # for reproducibility
x <- rnorm(10) # standard normal
x[1] <- x[1] * 10 # introduce outlier
## Winsorize data
x
Winsorize(x)
# use Large and Small, if a fix number of values should be winsorized (here k=3):
Winsorize(x, minval=tail(Small(x, k=3), 1), maxval=head(Large(x, k=3), 1))
``` |

```
[1] -12.0706575 0.2774292 1.0844412 -2.3456977 0.4291247 0.5060559
[7] -0.5747400 -0.5466319 -0.5644520 -0.8900378
[1] -7.6944256 0.2774292 0.8241678 -2.3456977 0.4291247 0.5060559
[7] -0.5747400 -0.5466319 -0.5644520 -0.8900378
[1] -0.8900378 0.2774292 0.4291247 -0.8900378 0.4291247 0.4291247
[7] -0.5747400 -0.5466319 -0.5644520 -0.8900378
```

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